Referring to the three cases distinguished in Section REF , problem (REF ): a) in the linear case, is also an LP, b) in the convex/concave case, is a CP, but c) in the concave/convex case, its constraint set is nonconvex: the intersection of sub-level sets of convex and concave functions. Hence, in the linear and the convex/concave case, problem (REF ) can be solved efficiently, to generate a tight suboptimality bound. In the concave/convex case, we could derive an upper bound on the optimal value of problem (REF ), e.g., by using an SMT solver [1]}.
